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Evolutionary insights into primate skeletal gene regulation using a comparative cell culture model

['Genevieve Housman', 'Section Of Genetic Medicine', 'Department Of Medicine', 'University Of Chicago', 'Chicago', 'Illinois', 'United States Of America', 'Emilie Briscoe', 'Yoav Gilad', 'Department Of Human Genetics']

Date: 2022-05

The evolution of complex skeletal traits in primates was likely influenced by both genetic and environmental factors. Because skeletal tissues are notoriously challenging to study using functional genomic approaches, they remain poorly characterized even in humans, let alone across multiple species. The challenges involved in obtaining functional genomic data from the skeleton, combined with the difficulty of obtaining such tissues from nonhuman apes, motivated us to consider an alternative in vitro system with which to comparatively study gene regulation in skeletal cell types. Specifically, we differentiated six human (Homo sapiens) and six chimpanzee (Pan troglodytes) induced pluripotent stem cell lines ( iPSCs ) into mesenchymal stem cells ( MSCs ) and subsequently into osteogenic cells (bone cells). We validated differentiation using standard methods and collected single-cell RNA sequencing data from over 100,000 cells across multiple samples and replicates at each stage of differentiation. While most genes that we examined display conserved patterns of expression across species, hundreds of genes are differentially expressed ( DE ) between humans and chimpanzees within and across stages of osteogenic differentiation. Some of these interspecific DE genes show functional enrichments relevant in skeletal tissue trait development. Moreover, topic modeling indicates that interspecific gene programs become more pronounced as cells mature. Overall, we propose that this in vitro model can be used to identify interspecific regulatory differences that may have contributed to skeletal trait differences between species.

Primates display a range of skeletal morphologies and susceptibilities to skeletal diseases, but the molecular basis of these phenotypic differences is unclear. Studies of gene expression variation in primate skeletal tissues are extremely restricted due to the ethical and practical challenges associated with collecting samples. Nevertheless, the ability to study gene regulation in primate skeletal tissues is crucial for understanding how the primate skeleton has evolved. We therefore developed a comparative primate skeletal cell culture model that allows us to access a spectrum of human and chimpanzee cell types as they differentiate from stem cells into bone cells. While most gene expression patterns are conserved across species, we also identified hundreds of differentially expressed genes between humans and chimpanzees within and across stages of differentiation. We also classified cells by osteogenic stage and identified additional interspecific differentially expressed genes which may contribute to skeletal trait differences. We anticipate that this model will be extremely useful for exploring questions related to gene regulation variation in primate bone biology and development.

Funding: This project received funding from NIH | National Institute of General Medical Sciences (NIGMS), grant number: R01GM122930 to Y.G.; and from NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), grant number: F32AR075397 to G.H. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability: All scRNA-seq data have been deposited in the NCBI’s Gene Expression Omnibus under the SuperSeries accession number GSE181744, which contains the SubSeries GSE167240 and GSE18174. All computational scripts and analysis pipelines can be found on GitHub at https://github.com/ghousman/human-chimp-skeletal-scRNA .

To examine skeletal gene expression among primates more effectively, we have established a comparative primate skeletal cell culture model that includes a large number of human (Homo sapiens) and chimpanzee (Pan troglodytes) iPSCs. Using this system, we collected and characterized single-cell RNA sequencing ( scRNA-seq ) data from different stages during differentiation towards osteogenic cells. Our study design allowed us to identify interspecific differences in gene expression, which may contribute to skeletal trait divergence between species.

As an alternative to in vivo skeletal tissues, induced pluripotent stem cell ( iPSC ) derived cell culture systems provide a new way to explore molecular variation in skeletal cell types. Previous studies have differentiated primate iPSCs into cranial neural crest cells ( CNCCs ), which are precursor cells that develop into a variety of tissues in the skull [ 29 , 30 ]. Additionally, protocols that differentiate iPSCs into osteoblasts [ 31 – 33 ], which are the primary cells in bone, do exist. However, most studies utilizing skeletal cell differentiation schemes include only 1–2 cell lines from humans or other model organisms, and often do not account for the purity of primary or differentiated cell cultures. Such study designs limit the evolutionary perspective and interpretation of the data generated.

Studying gene expression in skeletal tissues is challenging, as accessing bone and cartilage requires invasive procedures. In addition to the ethical and experimental challenges of collecting skeletal samples from living primates, the poor storage conditions of most skeletal remains that are available, make such samples unusable for most functional genomic applications, including the collection of gene expression data. Further, when well-preserved samples are available, the high cellular heterogeneity of tissues limits data interpretations. Perhaps because of these considerations, even large human transcriptomics consortia like GTEx [ 21 ] do not include data from bone and cartilage. Indeed, studies of human skeletal transcriptomics are limited to more targeted efforts to understand skeletal disease, and focus primarily on cartilage tissues and chondrogenic cell types [ 22 – 24 ]. Hence, while comparative primate functional genomics is a growing area of research [ 25 ], only a few groups have examined gene regulation in primate skeletal tissues [ 26 – 29 ]. Due to preservation issues, these studies predominantly focus on DNA methylation patterns as opposed to gene expression patterns.

The emergence of conserved and divergent skeletal phenotypes within the primate lineage is not fully resolved. Clarifying the mechanisms that contribute to such complex traits will improve our understanding of skeletal evolution and development. As with all complex traits, skeletal traits are affected by both genetic [ 11 – 15 ] and environmental factors [ 16 – 20 ], and these effects may be mediated, at least in part, through gene expression changes. While the contribution of environmental factors to skeletal differences has been widely studied in the fields of comparative anatomy, forensics, and paleoanthropology, molecular variation in skeletal tissues is not well characterized, especially among primates.

The skeleton is a biologically and evolutionarily important organ system that consists of several tissues, including bone and cartilage. The skeletal system serves a variety of functions, most notably supporting body weight and facilitating locomotion. While these broad functions are conserved across vertebrates, different species have developed distinct skeletal morphologies, which enable differential use of skeletal elements. For instance, certain bony feature shapes and sizes enable efficient bipedal locomotion in humans, while others enable efficient quadrupedal locomotion in certain nonhuman primates [ 1 , 2 ]. Primates also vary in their susceptibilities to different skeletal disorders, such as osteoarthritis [ 3 – 7 ] and osteoporosis [ 8 – 10 ].

Results

Identification of differentially expressed genes in our system Having established our iPSC-derived cell culture system as a reasonable model for studying primate osteogenesis, we then sought to understand how and to what extent the process of osteogenic differentiation differs between humans and chimpanzees. To do this, we first generated pseudobulk data by consolidating single-cell data originating from the same individual, replicate, and cell classification (Methods). Using the framework of a linear mixed model to account for the effects of species and cell line (Methods and Eq 1), we analyzed pseudobulk data to identify differentially expressed genes between humans and chimpanzees (interspecific DE genes) across each stage of differentiation. Initially, we defined differentiation stage relatively broadly, labeling cells as either pluripotent, mesenchymal, or osteogenic, as described above. Later, we took advantage of our single-cell data to study osteogenesis at a higher resolution, using two alternative approaches–an ad hoc candidate gene-based cell classification approach and a topic modeling strategy. We discovered hundreds of interspecific DE genes within each stage of differentiation (Fig 3). Using standard DE analyses of pseudobulk data (Methods and S10 Table), we found 2,098 interspecific DE genes in pluripotent cells, 904 in mesenchymal cells, and 446 in osteogenic cells at a false discovery rate (FDR) < 0.01 (Figs 3A and S14, and S11 Table). We considered the overlap between interspecific DE genes identified at different stages, initially by performing a pairwise comparison (S15 Fig). Although this pairwise approach is straightforward, it is not designed to capture dependence among multiple experimental conditions, and it is not ideal for detecting genes that are consistently DE but have small effect sizes. To address these issues, we also used Cormotif [45] to implement a Bayesian clustering approach capable of capturing the major patterns of correlation between interspecific DE genes identified at different stages of differentiation (Methods and S10 Table). Two common temporal expression patterns (or correlation motifs) best fit our pseudobulk data (Fig 3B). One motif notes a high degree of interspecific DE genes shared across all stages of differentiation, while a second contains interspecific DE genes unique to pluripotent cells. Using Cormotif, we detected 6,822 interspecific DE genes in pluripotent cells, 4,523 in mesenchymal cells, and 4,020 in osteogenic cells with a posterior probability > 0.65 (Figs 3B, S16, S17, and S12 Table). However, due to the high degree of sharing across stages of differentiation, only 2,759 are unique to pluripotent cells, 164 are unique to mesenchymal cells, and none are unique to osteogenic cells (S16 Fig). Overall, there is a decrease in interspecific DE genes as cells mature. PPT PowerPoint slide

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TIFF original image Download: Fig 3. Interspecific DE across three stages of osteogenic differentiation. Bar plot showing the number of interspecific DE genes identified for each stage of differentiation using standard methods (A). Correlation motifs based on the probability of differential expression between species for each stage of differentiation (B) and correlation motifs based on the probability of differential expression across stages of differentiations for each species (C) with the number of genes assigned to each motif shown in the bar plot on the right and the posterior probability that a gene is DE shown by the shading of each box. Box plots of the log2 transformed gene expression variance values for cells collected at each stage of differentiation for each species (*** p<0.001) (D). Enrichment of external DE gene sets among Cormotif interspecific DE genes identified for each stage of differentiation for validation (E) and functional interpretation (F) with the p-value (p.value), the number of DE genes overlapping an external gene set (DE.Interest), and the ratio of overlapping to non-overlapping DE genes for a given external gene set (GeneRatio) denoted. https://doi.org/10.1371/journal.pgen.1010073.g003 After measuring the degree to which interspecific DE genes are shared across the three stages of differentiation, we used Cormotif [45] to identify genes that are stage-specific; that is, genes that are DE between subsequent stages of differentiation. We then asked whether stage-specific genes are generally conserved among humans and chimpanzees. We identified four correlation motifs that best fit our pseudobulk data (Fig 3C) and found that the majority (about 89%) of stage-specific DE genes are conserved between species (S13 Table). Of the remaining divergent stage-specific DE genes, we detected 20 between pluripotent and mesenchymal cells and 266 between mesenchymal and osteogenic cells, which was somewhat unexpected given that there are substantially fewer stage-specific DE genes at later stages of differentiation than at earlier stages of differentiation. Additionally, this finding was surprising given our previous observation that there are fewer interspecific DE genes in osteogenic cells, which had initially suggested to us that gene expression patterns are more conserved in osteogenic cells than in early-stage cells. Another possible explanation for the observed decrease in interspecific DE over time is that DE is more difficult to detect in late-stage cells due to increased levels of gene expression variation. Indeed, examining a total of 11,579 genes, we found a significant increase in gene expression variance between pluripotent cells and mesenchymal cells and between mesenchymal cells and osteogenic cells–a pattern that is maintained across species (Figs 3D and S18). However, since there is no a priori reason to expect mesenchymal and osteogenic cells to have higher gene expression variance than other cell types, we hypothesized that the high variance we observe is more likely due to increased cell heterogeneity over the course of differentiation. Thus, our subsequent analyses specifically address this property of the data.

Concordance of interspecific DE genes with previous studies of differential gene regulation between humans and chimpanzees Previous studies have identified genes that are differentially regulated between humans and chimpanzees in a number of tissue types [29,36,47,48]. We performed gene set enrichment tests to assess concordance between the interspecific DE genes we identified in this study and previously identified genes. For these analyses, we focused on the interspecific DE genes identified using Cormotif. First, we considered the interspecific DE genes we identified using the three-stage classification approach (S15 Table and Figs 3E, 3F, and S23). As expected, genes previously identified in iPSCs as DE between species [36,48] are enriched among genes we identified as interspecific DE in pluripotent cells (all P < 0.002) but not among the interspecific DE genes we identified in mesenchymal or osteogenic cells (all P > 0.66). By and large, genes previously identified as DE between species in non-pluripotent, non-mesenchymal, and non-osteogenic cell types and tissues [36,47] are not enriched among genes we identified as DE between species. Still, we found that interspecific DE genes previously identified in kidney tissue [47] are enriched among genes we identified as interspecific DE in pluripotent cells (P < 0.006)–an enrichment that persists regardless of the posterior probability threshold used to classify significant interspecific DE genes (S17 Fig). Lastly, while there are no previous studies of gene expression differences between humans and chimpanzees in mesenchymal or osteogenic cells, there has been research on gene expression in related CNCCs [48] and on differentially methylated regions (DMRs) in bone tissues [29]. Although we observed overlap of these external gene sets with our interspecific DE genes, we did not identify significant enrichments, even when examining all interspecific DE genes identified in osteogenic cells regardless of sharing (all P > 0.65; S24 Fig). However, previously identified interspecific DE genes in CNCCs are slightly enriched among genes we identified as interspecific DE in later stages of differentiation (P < 0.06). Next, we performed gene set enrichment tests to assess concordance between previously annotated external DE gene sets [29,36,47,48] and the interspecific DE genes we identified across five stages of osteogenesis (Figs 4E, 4F, and S23 and S15 Table). As expected, genes previously identified as DE between species in iPSCs [36,48] and in alternative cell types and tissues [36,47] are not enriched among the interspecific DE genes we identified across stages of osteogenesis. Conversely, genes previously found to be differentially regulated between humans and chimpanzees in skeleton-related cell types and tissues do overlap our interspecific DE genes. That is, interspecific DE genes identified in iPSC-derived CNCCs [48] and interspecific DMR-associated genes identified in bone [29] are slightly enriched among interspecific DE genes shared across all stages of osteogenesis (P < 0.03 and P < 0.01, respectively). Again, it appears that there was a benefit to using a higher-resolution osteogenic cell classification system, as we were able to identify more skeletally relevant functional enrichments when we grouped cells by osteogenic stage rather than bulking them together based on a general collection time point.

[END]

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